From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

SWOT Hydrology Dataset Exploration on a local machine

Accessing and Visualizing SWOT Datasets

Requirement:

Local compute environment e.g. laptop, server: this tutorial can be run on your local machine.

Learning Objectives:

• Access SWOT HR data prodcuts (archived in NASA Earthdata Cloud) within the AWS cloud, by downloading to local machine
• Visualize accessed data for a quick check

SWOT Level 2 KaRIn High Rate Version 1.1 (where available) Datasets:

1. River Vector Shapefile - SWOT_L2_HR_RIVERSP_1.1

2. Lake Vector Shapefile - SWOT_L2_HR_LAKESP_1.1

3. Water Mask Pixel Cloud NetCDF - SWOT_L2_HR_PIXC_1.1

4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_L2_HR_PIXCVec_1.1

5. Raster NetCDF - SWOT_L2_HR_Raster_1.1

6. Single Look Complex Data product - SWOT_L1B_HR_SLC_1.1

Notebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2023) || Other Contributors: Zoe Walschots (PO.DAAC Summer Intern 2023), Catalina Taglialatela (NASA PO.DAAC), Luis Lopez (NASA NSIDC DAAC)

Last updated: 4 Dec 2023

Libraries Needed

import glob
import os
import requests
import netCDF4 as nc
import h5netcdf
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import zipfile
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. If you don’t already have one, please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

auth = earthaccess.login()

EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)
You're now authenticated with NASA Earthdata Login
Using token with expiration date: 12/22/2023
Using .netrc file for EDL

Single File Access

1. River Vector Shapefiles

The https access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.

Alternatively, Earthdata Search (see tutorial) can be used to manually search in a GUI interface.

Search for the data of interest

#Retrieves granule from the day we want, in this case by passing to `earthdata.search_data` function the data collection shortname, temporal bounds, filter by wildcards, and for restricted data one must specify the search count
river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_1.1', 
                                        temporal = ('2023-04-08 00:00:00', '2023-04-25 23:59:59'),
                                        granule_name = '*Reach*_013_NA*') # here we filter by Reach files (not node), pass #13 and continent code=NA

Granules found: 17

Dowload, unzip, read the data

Let’s download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.

earthaccess.download([river_results[0]], "./data_downloads")

 Getting 1 granules, approx download size: 0.0 GB

['SWOT_L2_HR_RiverSP_Reach_484_013_NA_20230408T071821_20230408T071832_PIB0_01.zip']

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we’ll programmatically get the filename we just downloaded, and then extract all data to the data_downloads folder.

filename = earthaccess.results.DataGranule.data_links(river_results[0], access='external')
filename = filename[0].split("/")[-1]
filename

'SWOT_L2_HR_RiverSP_Reach_484_013_NA_20230408T071821_20230408T071832_PIB0_01.zip'

with zipfile.ZipFile(f'data_downloads/{filename}', 'r') as zip_ref:
    zip_ref.extractall('data_downloads')

Open the shapefile using geopandas

filename_shp = filename.replace('.zip','.shp')

SWOT_HR_shp1 = gpd.read_file(f'data_downloads/{filename_shp}') 

#view the attribute table
SWOT_HR_shp1 

	reach_id	time	time_tai	time_str	p_lat	p_lon	river_name	wse	wse_u	wse_r_u	...	p_wid_var	p_n_nodes	p_dist_out	p_length	p_maf	p_dam_id	p_n_ch_max	p_n_ch_mod	p_low_slp	geometry
0	77460000013	7.342535e+08	7.342536e+08	2023-04-08T07:18:42Z	40.621824	-124.244823	Eel River	2.320100e+00	9.653000e-02	3.490000e-02	...	23702.805	96	19376.986	19194.609276	-1.000000e+12	0	4	1	0	LINESTRING (-124.29069 40.66364, -124.29104 40...
1	77460000021	7.342535e+08	7.342536e+08	2023-04-08T07:18:42Z	40.542406	-124.156177	Eel River	9.248800e+00	9.071000e-02	1.130000e-02	...	3435.524	51	29478.836	10101.849934	-1.000000e+12	0	2	1	0	LINESTRING (-124.16119 40.58421, -124.16097 40...
2	77460000031	7.342535e+08	7.342536e+08	2023-04-08T07:18:42Z	40.494638	-124.107178	Eel River	1.970160e+01	5.754600e-01	5.683800e-01	...	1202.549	50	39553.707	10074.871060	-1.000000e+12	0	2	1	0	LINESTRING (-124.13864 40.50871, -124.13829 40...
3	77460000041	7.342535e+08	7.342536e+08	2023-04-08T07:18:42Z	40.447111	-124.021272	Eel River	3.471650e+01	1.486718e+01	1.486691e+01	...	645.984	81	55843.425	16289.718636	-1.000000e+12	0	1	1	0	LINESTRING (-124.09611 40.46269, -124.09575 40...
4	77460000051	7.342535e+08	7.342536e+08	2023-04-08T07:18:42Z	40.395990	-123.930243	Eel River	3.229870e+01	9.148000e-02	1.638000e-02	...	1532.658	51	66073.910	10230.484650	-1.000000e+12	0	2	1	0	LINESTRING (-123.95755 40.42295, -123.95719 40...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
355	78322900143	-1.000000e+12	-1.000000e+12	no_data	50.998910	-119.011806	no_data	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	116183.841	15	59585.535	2935.007230	-1.000000e+12	0	1	1	0	LINESTRING (-119.03134 51.00406, -119.03105 51...
356	78322900153	7.342537e+08	7.342537e+08	2023-04-08T07:21:51Z	51.047442	-119.042215	no_data	3.451806e+02	1.114110e+00	1.110470e+00	...	41059.043	51	30434.699	10168.113068	-1.000000e+12	0	2	1	0	LINESTRING (-119.03140 51.09147, -119.03165 51...
357	78322900173	-1.000000e+12	-1.000000e+12	no_data	50.916115	-119.036919	no_data	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	186564.361	98	39488.857	19642.879384	-1.000000e+12	0	4	1	0	LINESTRING (-118.99201 50.99421, -118.99200 50...
358	78322900183	7.342537e+08	7.342537e+08	2023-04-08T07:21:52Z	51.060235	-118.938606	no_data	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	177029.325	86	56650.527	17161.670624	-1.000000e+12	0	4	1	0	LINESTRING (-118.99200 50.99394, -118.99201 50...
359	78322900211	-1.000000e+12	-1.000000e+12	no_data	50.549433	-119.062048	Shuswap River	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	122.690	50	103956.987	10074.432813	-1.000000e+12	0	2	1	0	LINESTRING (-119.11242 50.54684, -119.11207 50...

360 rows × 127 columns

Quickly plot the SWOT river data

# Simple plot
fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp1.plot(ax=ax, color='black')

# # Another way to plot geopandas dataframes is with `explore`, which also plots a basemap
# SWOT_HR_shp1.explore()

2. Lake Vector Shapefiles

The lake vector shapefiles can be accessed in the same way as the river shapefiles above.

Search for data of interest

lake_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_LAKESP_1.1', 
                                       temporal = ('2023-04-08 00:00:00', '2023-04-25 23:59:59'),
                                       granule_name = '*Obs*_013_NA*') # here we filter by files with 'Obs' in the name (This collection has three options: Obs, Unassigned, and Prior), pass #13 and continent code=NA

Granules found: 17

Let’s download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.

earthaccess.download([lake_results[0]], "./data_downloads")

 Getting 1 granules, approx download size: 0.0 GB

['SWOT_L2_HR_LakeSP_Obs_484_013_NA_20230408T071821_20230408T072230_PIB0_01.zip']

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we’ll programmatically get the filename we just downloaded, and then extract all data to the SWOT_downloads folder.

filename2 = earthaccess.results.DataGranule.data_links(lake_results[0], access='external')
filename2 = filename2[0].split("/")[-1]
filename2

'SWOT_L2_HR_LakeSP_Obs_484_013_NA_20230408T071821_20230408T072230_PIB0_01.zip'

with zipfile.ZipFile(f'data_downloads/{filename2}', 'r') as zip_ref:
    zip_ref.extractall('data_downloads')

Open the shapefile using geopandas

filename_shp2 = filename2.replace('.zip','.shp')
filename_shp2

'SWOT_L2_HR_LakeSP_Obs_484_013_NA_20230408T071821_20230408T072230_PIB0_01.shp'

SWOT_HR_shp2 = gpd.read_file(f'data_downloads/{filename_shp2}') 

#view the attribute table
SWOT_HR_shp2

	obs_id	lake_id	overlap	n_overlap	reach_id	time	time_tai	time_str	wse	wse_u	...	load_tidef	load_tideg	pole_tide	dry_trop_c	wet_trop_c	iono_c	xovr_cal_c	lake_name	p_res_id	geometry
0	781229R000006	7810001133	85	1	no_data	7.342536e+08	7.342536e+08	2023-04-08T07:19:21Z	593.993	0.011	...	-0.019138	-0.018407	-0.002311	-2.164411	-0.074947	-0.003339	-1.000000e+12	APPLEGATE RESERVOIR;APPLEGATE LAKE	116	MULTIPOLYGON (((-123.10728 42.03437, -123.1073...
1	782232R000018	7820002572;7820002253	86;80	2	no_data	7.342536e+08	7.342536e+08	2023-04-08T07:19:51Z	439.811	0.167	...	-0.016663	-0.016105	-0.002303	-2.199668	-0.069287	-0.003153	-1.000000e+12	HILLS CREEK RESERVOIR	-99999999	MULTIPOLYGON (((-122.45387 43.68914, -122.4539...
2	782243R000013	7820039162	10	1	no_data	7.342537e+08	7.342537e+08	2023-04-08T07:21:40Z	341.457	0.003	...	-0.009852	-0.009920	-0.002187	-2.211506	-0.079803	-0.002515	-1.000000e+12	WHITEMAN CREEK;OKANAGAN LAKE;OKANAGAN	-99999999	MULTIPOLYGON (((-119.72285 49.72639, -119.7227...
3	782243R015586	7820039162	17	1	no_data	7.342537e+08	7.342537e+08	2023-04-08T07:21:48Z	341.513	0.028	...	-0.009454	-0.009599	-0.002172	-2.209661	-0.079193	-0.002491	-1.000000e+12	WHITEMAN CREEK;OKANAGAN LAKE;OKANAGAN	-99999999	MULTIPOLYGON (((-119.49606 50.06182, -119.4961...
4	783245R000005	7830263943	32	1	78322700063;78322700091;78322800011;7832290001...	7.342537e+08	7.342537e+08	2023-04-08T07:22:01Z	344.832	0.014	...	-0.009029	-0.009206	-0.002157	-2.207273	-0.082830	-0.002465	-1.000000e+12	ROSS CREEK;EAGLE RIVER;SHUSWAP;LITTLE RIVER;SC...	-99999999	MULTIPOLYGON (((-119.35814 50.94441, -119.3580...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
831	783246R009013	7830249372;7830249352	81;71	2	no_data	7.342537e+08	7.342538e+08	2023-04-08T07:22:13Z	1837.232	0.563	...	-0.008546	-0.008791	-0.002133	-2.094712	-0.066065	-0.002440	-1.000000e+12	no_data	-99999999	MULTIPOLYGON (((-118.98292 51.59811, -118.9830...
832	783246R009931	7830249332	87	1	no_data	7.342537e+08	7.342538e+08	2023-04-08T07:22:14Z	1635.514	0.325	...	-0.008539	-0.008785	-0.002132	-2.136184	-0.071612	-0.002440	-1.000000e+12	no_data	-99999999	POLYGON ((-118.98463 51.61215, -118.98453 51.6...
833	783246R010740	7830243082	82	1	no_data	7.342537e+08	7.342538e+08	2023-04-08T07:22:14Z	2094.295	5.792	...	-0.008558	-0.008815	-0.002135	-1.942940	-0.048756	-0.002438	-1.000000e+12	no_data	-99999999	MULTIPOLYGON (((-119.07615 51.67504, -119.0759...
834	783246R010910	7830256902	21	1	no_data	7.342537e+08	7.342538e+08	2023-04-08T07:22:14Z	1996.670	0.303	...	-0.008524	-0.008775	-0.002132	-1.863152	-0.041920	-0.002443	-1.000000e+12	no_data	-99999999	POLYGON ((-118.97489 51.63692, -118.97478 51.6...
835	782246R012796	7820071012	75	1	no_data	7.342537e+08	7.342538e+08	2023-04-08T07:22:15Z	1962.504	0.385	...	-0.008469	-0.008730	-0.002128	-2.104326	-0.068847	-0.002438	-1.000000e+12	no_data	-99999999	POLYGON ((-118.88701 51.66364, -118.88710 51.6...

836 rows × 36 columns

Quickly plot the SWOT lakes data

fig, ax = plt.subplots(figsize=(7,5))
SWOT_HR_shp2.plot(ax=ax, color='black')

Accessing the remaining files is different than the shp files above. We do not need to extract the shapefiles from a zip file because the following SWOT HR collections are stored in netCDF files in the cloud. For the rest of the products, we will open via xarray, not geopandas.

3. Water Mask Pixel Cloud NetCDF

Search for data collection and time of interest

pixc_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXC_1.1',
                                       temporal = ('2023-04-22 00:00:00', '2023-04-23 23:59:59'), 
                                       granule_name = '*_499_013_*') # here we filter by cycle=499 and pass=013

Granules found: 164

Let’s download one data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass.

earthaccess.download([pixc_results[100]], "./data_downloads")

 Getting 1 granules, approx download size: 0.41 GB

['SWOT_L2_HR_PIXC_499_013_246R_20230423T050139_20230423T050150_PIB0_01.nc']

Open data using xarray

The pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.

ds_PIXC = xr.open_mfdataset("data_downloads/SWOT_L2_HR_PIXC_*.nc", group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC

<xarray.Dataset>
Dimensions:                                (points: 4235768, complex_depth: 2,
                                            num_pixc_lines: 3238)
Coordinates:
    latitude                               (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    longitude                              (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
Dimensions without coordinates: points, complex_depth, num_pixc_lines
Data variables: (12/57)
    azimuth_index                          (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    range_index                            (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    interferogram                          (points, complex_depth) float32 dask.array<chunksize=(4235768, 2), meta=np.ndarray>
    power_plus_y                           (points) float32 dask.array<chunksize=(4235768,), meta=np.ndarray>
    power_minus_y                          (points) float32 dask.array<chunksize=(4235768,), meta=np.ndarray>
    coherent_power                         (points) float32 dask.array<chunksize=(4235768,), meta=np.ndarray>
    ...                                     ...
    interferogram_qual                     (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    classification_qual                    (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    geolocation_qual                       (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    sig0_qual                              (points) float64 dask.array<chunksize=(4235768,), meta=np.ndarray>
    pixc_line_qual                         (num_pixc_lines) float64 dask.array<chunksize=(3238,), meta=np.ndarray>
    pixc_line_to_tvp                       (num_pixc_lines) float32 dask.array<chunksize=(3238,), meta=np.ndarray>
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3238
    interferogram_size_range:    5558
    looks_to_efflooks:           1.530934891296426
    num_azimuth_looks:           7.0
    azimuth_offset:              9

xarray.Dataset

• Dimensions:
  • points: 4235768
  • complex_depth: 2
  • num_pixc_lines: 3238
• Coordinates: (2)
  • latitude
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    quality_flag :

    geolocation_qual

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the pixel.

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    quality_flag :

    geolocation_qual

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (57)
  • azimuth_index
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    rare interferogram azimuth index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram azimuth index (indexed from 0).

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • range_index
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    rare interferogram range index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram range index (indexed from 0).

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • interferogram
    (points, complex_depth)
    float32
    dask.array<chunksize=(4235768, 2), meta=np.ndarray>

    long_name :

    rare interferogram

    units :

    1

    quality_flag :

    interferogram_qual

    valid_min :

    -1e+20

    valid_max :

    1e+20

    comment :

    Complex unflattened rare interferogram.

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768, 2) (4235768, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • power_plus_y
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    power for plus_y channel

    units :

    1

    quality_flag :

    interferogram_qual

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • power_minus_y
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    power for minus_y channel

    units :

    1

    quality_flag :

    interferogram_qual

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • coherent_power
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    coherent power combination of minus_y and plus_y channels

    units :

    1

    quality_flag :

    interferogram_qual

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • x_factor_plus_y
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    X factor for plus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • x_factor_minus_y
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    X factor for minus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • water_frac
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    water fraction

    units :

    1

    quality_flag :

    classification_qual

    valid_min :

    -1000.0

    valid_max :

    10000.0

    comment :

    Noisy estimate of the fraction of the pixel that is water.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • water_frac_uncert
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    water fraction uncertainty

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • classification
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    classification

    quality_flag :

    classification_qual

    flag_meanings :

    land land_near_water water_near_land open_water dark_water low_coh_water_near_land open_low_coh_water

    flag_values :

    [1 2 3 4 5 6 7]

    valid_min :

    1

    valid_max :

    7

    comment :

    Flags indicating water detection results.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • false_detection_rate
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    false detection rate

    units :

    1

    quality_flag :

    classification_qual

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting water when there is none.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • missed_detection_rate
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    missed detection rate

    units :

    1

    quality_flag :

    classification_qual

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting no water when there is water.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prior_water_prob
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    prior water probability

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Prior probability of water occurring.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bright_land_flag
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    bright land flag

    standard_name :

    status_flag

    flag_meanings :

    not_bright_land bright_land bright_land_or_water

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • layover_impact
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    layover impact

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • eff_num_rare_looks
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    effective number of rare looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken to form the rare interferogram.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • height
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    height above reference ellipsoid

    units :

    m

    quality_flag :

    geolocation_qual

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Height of the pixel above the reference ellipsoid.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cross_track
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    approximate cross-track location

    units :

    m

    quality_flag :

    geolocation_qual

    valid_min :

    -75000.0

    valid_max :

    75000.0

    comment :

    Approximate cross-track location of the pixel.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pixel_area
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    pixel area

    units :

    m^2

    quality_flag :

    geolocation_qual

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Pixel area.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • inc
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    incidence angle

    units :

    degrees

    quality_flag :

    geolocation_qual

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Incidence angle.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • phase_noise_std
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    phase noise standard deviation

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the phase noise standard deviation.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dlatitude_dphase
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of latitude estimate to interferogram phase

    units :

    degrees/radian

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the latitude estimate to the interferogram phase.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dlongitude_dphase
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of longitude estimate to interferogram phase

    units :

    degrees/radian

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the longitude estimate to the interferogram phase.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_dphase
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to interferogram phase

    units :

    m/radian

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferogram phase.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_droll
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to spacecraft roll

    units :

    m/degrees

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the spacecraft roll.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_dbaseline
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to interferometric baseline

    units :

    m/m

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferometric baseline.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_drange
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to range (delay)

    units :

    m/m

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the range (delay).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • darea_dheight
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sensitivity of pixel area to reference height

    units :

    m^2/m

    quality_flag :

    geolocation_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the pixel area to the reference height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • illumination_time
    (points)
    datetime64[ns]
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    time of illumination of each pixel (UTC)

    standard_name :

    time

    tai_utc_difference :

    37.0

    leap_second :

    0000-00-00T00:00:00Z

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • illumination_time_tai
    (points)
    datetime64[ns]
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    time of illumination of each pixel (TAI)

    standard_name :

    time

    comment :

    Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • eff_num_medium_looks
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    effective number of medium looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sig0
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sigma0

    units :

    1

    quality_flag :

    sig0_qual

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sig0_uncert
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    sigma0 uncertainty

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    1-sigma uncertainty in the sig0 measurement. The value is given as an additive (not multiplicative) linear term (not a term in decibels).

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • phase_unwrapping_region
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    phase unwrapping region index

    units :

    1

    valid_min :

    -1

    valid_max :

    99999999

    comment :

    Phase unwrapping region index.

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ambiguity_cost1
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    phase ambiguity minimum cost

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Phase ambiguity minimum cost.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ambiguity_cost2
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    phase ambiguity 2nd minimum cost

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Phase ambiguity 2nd minimum cost.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_range_cor
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    instrument range correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to range before geolocation.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_phase_cor
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    instrument phase correction

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to phase before geolocation.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_baseline_cor
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    instrument baseline correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to baseline before geolocation.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sig0_cor_atmos_model
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    two-way atmospheric correction to sigma0 from model

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    1

    valid_min :

    1.0

    valid_max :

    10.0

    comment :

    Atmospheric correction to sigma0 from weather model data as a linear power multiplier (not decibels). sig0_cor_atmos_model is already applied in computing sig0 and x_factor_plus_y and x_factor_minus_y.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • height_cor_xover
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    height correction from KaRIn crossovers

    units :

    m

    valid_min :

    -10.0

    valid_max :

    10.0

    comment :

    Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • model_dry_tropo_cor
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    dry troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -3.0

    valid_max :

    -1.5

    comment :

    Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • model_wet_tropo_cor
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    wet troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -1.0

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • iono_cor_gim_ka
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    ionosphere vertical correction

    source :

    Global Ionosphere Maps

    institution :

    JPL

    units :

    m

    valid_min :

    -0.5

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geoid
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    geoid height

    standard_name :

    geoid_height_above_reference_ellipsoid

    source :

    EGM2008 (Pavlis et al., 2012)

    units :

    m

    valid_min :

    -150.0

    valid_max :

    150.0

    comment :

    Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency). This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • solid_earth_tide
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    solid Earth tide height

    source :

    Cartwright and Taylor (1971) and Cartwright and Edden (1973)

    units :

    m

    valid_min :

    -1.0

    valid_max :

    1.0

    comment :

    Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • load_tide_fes
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    geocentric load tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • load_tide_got
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    geocentric load tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pole_tide
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    geocentric pole tide height

    source :

    Wahr (1985) and Desai et al. (2015)

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust). This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ancillary_surface_classification_flag
    (points)
    float32
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    long_name :

    surface classification

    standard_name :

    status_flag

    source :

    MODIS/GlobCover

    institution :

    European Space Agency

    flag_meanings :

    open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin

    flag_values :

    [0 1 2 3 4 5 6]

    valid_min :

    0

    valid_max :

    6

    comment :

    7-state surface type classification computed from a mask built with MODIS and GlobCover data.

    Array Chunk Bytes 16.16 MiB 16.16 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • interferogram_qual
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    rare_power_suspect rare_phase_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded rare_power_bad rare_phase_bad tvp_bad sc_event_bad large_karin_gap

    flag_masks :

    [ 2048 4096 8192 16384 32768 262144 524288 134217728 268435456 536870912 1073741824 2147483648]

    valid_min :

    0

    valid_max :

    4161599488

    comment :

    Quality flag for the interferogram quantities in the pixel cloud data

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • classification_qual
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    no_coherent_gain power_close_to_noise_floor detected_water_but_no_prior_water detected_water_but_bright_land water_false_detection_rate_suspect coherent_power_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded coherent_power_bad tvp_bad sc_event_bad large_karin_gap

    flag_masks :

    [ 1 2 4 8 16 2048 8192 16384 32768 262144 524288 134217728 536870912 1073741824 2147483648]

    valid_min :

    0

    valid_max :

    3893159967

    comment :

    Quality flag for the classification quantities in the pixel cloud data

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • geolocation_qual
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    layover_significant phase_noise_suspect phase_unwrapping_suspect model_dry_tropo_cor_suspect model_wet_tropo_cor_suspect iono_cor_gim_ka_suspect xovercal_suspect medium_phase_suspect tvp_suspect sc_event_suspect small_karin_gap specular_ringing_degraded model_dry_tropo_cor_missing model_wet_tropo_cor_missing iono_cor_gim_ka_missing xovercal_missing geolocation_is_from_refloc no_geolocation_bad medium_phase_bad tvp_bad sc_event_bad large_karin_gap

    flag_masks :

    [ 1 2 4 8 16 32 64 4096 8192 16384 32768 524288 1048576 2097152 4194304 8388608 16777216 134217728 268435456 536870912 1073741824 2147483648]

    valid_min :

    0

    valid_max :

    4193841279

    comment :

    Quality flag for the geolocation quantities in the pixel cloud data

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sig0_qual
    (points)
    float64
    dask.array<chunksize=(4235768,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    sig0_uncert_suspect sig0_cor_atmos_suspect noise_power_suspect xfactor_suspect rare_power_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded sig0_cor_atmos_missing noise_power_bad xfactor_bad rare_power_bad tvp_bad sc_event_bad large_karin_gap

    flag_masks :

    [ 1 2 4 8 2048 8192 16384 32768 262144 524288 1048576 33554432 67108864 134217728 536870912 1073741824 2147483648]

    valid_min :

    0

    valid_max :

    3994871823

    comment :

    Quality flag for sig0

    Array Chunk Bytes 32.32 MiB 32.32 MiB Shape (4235768,) (4235768,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pixc_line_qual
    (num_pixc_lines)
    float64
    dask.array<chunksize=(3238,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    not_in_tile tvp_suspect sc_event_suspect small_karin_gap tvp_bad sc_event_bad large_karin_gap

    flag_masks :

    [ 1 8192 16384 32768 536870912 1073741824 2147483649]

    valid_min :

    0

    valid_max :

    3758153729

    comment :

    Quality flag for pixel cloud data per rare-posted interferogram line (similar to slc_qual in the L1B_HR_SLC product)

    Array Chunk Bytes 25.30 kiB 25.30 kiB Shape (3238,) (3238,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pixc_line_to_tvp
    (num_pixc_lines)
    float32
    dask.array<chunksize=(3238,), meta=np.ndarray>

    long_name :

    pixel cloud rare line to tvp index

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Pixel cloud rare radar grid line index to tvp index mapping

    Array Chunk Bytes 12.65 kiB 12.65 kiB Shape (3238,) (3238,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

Indexes: (0)

Attributes: (6)

description :

cloud of geolocated interferogram pixels

interferogram_size_azimuth :

3238

interferogram_size_range :

5558

looks_to_efflooks :

1.530934891296426

num_azimuth_looks :

7.0

azimuth_offset :

9